Converters and Visualizer

Input file as a path object.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Target file format.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

MutableMapping[str, Any]: Converted file as a dictionary.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Dict[str, Any]: Return the input file arguments as a dictionary without additional information beyond the command line arguments.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

If the output file format is not supported.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Data to save.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Filename of the target file.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Target file format.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The input file as a path object.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The output file format.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

If the input file format is not supported.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

MutableMapping[str, Any] : The converted file as a dictionary.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Dict[str, Any]: Return the input file arguments as a dictionary without additional information beyond the command line arguments.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

If the input file format is identical with the output format.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

If the output file format is not supported.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The converted file as a dictionary.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The input file as a path object.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The output file format.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Input file as a path object.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Target file format.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

If the file format is not supported.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

MutableMapping[str, Any]: The converted data as a MutableMapping[str, Any], which belongs to DataFrame.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Dict[str, Any]: Return the input file arguments as a dictionary without additional information beyond the command line arguments.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

If the export format is not supported.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The converted data, which is a pandas DataFrame.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The file name of the data file.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The file format of the exported file.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The data to export.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The filename of the output file.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The file format of the output file.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The data to convert.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

List[Dict[str, Any]]: The converted data.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The input file of the as a path object.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

The output file format.

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting

Dict[str, Any]: The data as a dictionary, which can be a nested dictionary

_[API]: Application Programming Interface _[ASCII]: American Standard Code for Information Interchange; a common plain text format _[Amplitude]: The height or maximum value of a peak function _[Athena]: Interactive graphical utility for XAS analysis based on IFEFFIT or LARCH _[Automatic Peak Detection]: Automatically finding peaks in the data using algorithms like find_peaks _[Background Subtraction]: Removing the underlying signal or baseline from the raw data _[Baseline]: The underlying signal or background upon which spectral features are superimposed _[Baseline Correction]: The process of estimating and removing the baseline signal _[Bayesian Inference]: Statistical method used for updating probabilities based on evidence, applicable in parameter estimation _[Bounds]: The lower and upper limits constraining a Parameter during optimization _[Calibration]: Process of correlating the readings of an instrument with a standard to ensure accuracy, e.g., energy calibration _[Center]: The position of the maximum of a peak function along the independent variable axis (e.g., energy, wavenumber) _[CI/CD]: Continuous Integration/Continuous Deployment _[CLI]: Command Line Interface _[CSV]: Comma-Separated Values file format _[Chi-squared (χ²)]: A statistical measure of the Goodness-of-fit between observed data and a Model _[Confidence Intervals]: A range of values providing an estimate of the uncertainty of a fitted Parameter _[Configuration File]: A file containing settings and parameters for software, often in formats like JSON, YAML, or TOML _[Convergence]: The state reached when an iterative optimization algorithm finds a stable solution where further iterations do not significantly improve the fit _[Conventional Commits]: A specification for adding human and machine readable meaning to commit messages _[Convolution]: A mathematical operation combining two functions to produce a third function, expressing how the shape of one is modified by the other; relevant for Voigt profiles _[Correlation]: A statistical measure indicating the extent to which two fitted Parameters move in relation to each other _[Covariance Matrix]: A matrix whose elements represent the covariances between pairs of fitted Parameters, used to estimate uncertainties _[Curve Fitting]: The process of constructing a curve, or mathematical function, that has the best fit to a series of data points _[Data Acquisition]: The process of collecting data from an experiment or instrument _[Data Model API]: Application programming interface that allows interaction with the data model defined using Pydantic _[Deconvolution]: Process of resolving overlapping peaks or removing instrumental broadening effects _[Degrees of Freedom (DoF)]: The number of independent pieces of information available to estimate another quantity; typically number of data points minus number of varied parameters _[Derivative]: The rate of change of a function; used in spectroscopy for peak finding or enhancing features _[Detector]: Device used to measure the intensity of radiation in spectroscopic experiments _[Dictionaries]: Collection of key-value pairs in Python _[Diffraction]: The bending of waves as they pass around an obstacle or through an aperture, relevant in X-ray techniques _[Docker]: Platform for developing, shipping, and running applications in containers _[Emission]: Process by which a substance releases energy in the form of electromagnetic radiation _[Energy]: Physical quantity often used as the independent variable (x-axis) in spectroscopic measurements (e.g., eV, keV) _[Environment (Virtual)]: Isolated Python environment managing dependencies for a specific project (e.g., using venv, Conda, Poetry) _[Excel]: Spreadsheet software developed by Microsoft _[Expressions]: Mathematical constraints or dependencies between different fitting Parameters, evaluated by lmfit _[Fluorescence]: Emission of light by a substance that has absorbed light or other electromagnetic radiation _[Fourier Transform]: Mathematical transform used to decompose a function into its constituent frequencies, used in FT-IR spectroscopy _[Frequency]: Number of occurrences of a repeating event per unit of time, related to energy and wavelength _[FWHM]: Full Width at Half Maximum; a measure of the width of a peak function (e.g., fwhmg, fwhml) _[Gaussian]: A common bell-shaped peak function used in curve fitting _[Git]: Distributed version control system _[GitHub]: Platform for hosting Git repositories and collaboration _[GUI]: Graphical User Interface _[Global Fitting]: Simultaneously fitting multiple datasets with shared Parameters _[Goodness-of-fit]: Statistical measures (e.g., Chi-squared, R-squared) indicating how well a Model fits the observed data _[IDE]: Integrated Development Environment _[Initial Values]: The starting values assigned to Parameters before the optimization process begins _[Intensity]: The dependent variable (y-axis) in spectroscopy, representing the strength of the signal _[Integration]: Calculating the area under a curve, often used to quantify peak intensity _[IR]: Infrared Spectroscopy _[Iteration]: A single step or cycle within an optimization algorithm _[JSON]: JavaScript Object Notation _[Jupyter Notebook]: Web-based interactive computing environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text _[JupyterLab]: Next-generation web-based user interface for Project Jupyter _[L-edge]: Features in X-ray Absorption Spectroscopy (XAS) arising from electron transitions from the L atomic shell (n=2) _[Least-Squares]: An optimization method that minimizes the sum of the squares of the Residuals between observed data and a Model _[Levenberg-Marquardt]: An iterative algorithm used for solving non-linear Least-Squares problems, the default Optimizer in lmfit _[Lorentzian]: A common peak function, also known as the Cauchy distribution, used in curve fitting _[Matplotlib]: Plotting library for the Python programming language _[Maximum Likelihood Estimation (MLE)]: Method for estimating the parameters of a statistical model given observations _[Metadata]: Data that provides information about other data (e.g., experimental conditions, sample details) _[Minimizer]: The lmfit class used for defining and performing the optimization (fitting) problem _[MkDocs Material]: A theme for the MkDocs static site generator, used for SpectraFit's documentation _[Model]: A mathematical function used to describe the data, often composed of one or more peak functions and a baseline, implemented using lmfit Models _[Model Component]: A distinct part of a composite Model, such as an individual peak or a baseline function _[Monte Carlo Methods]: Computational algorithms relying on repeated random sampling to obtain numerical results, sometimes used in error estimation _[Nelder-Mead]: A simplex-based optimization algorithm available in lmfit _[NetworkX]: Python library for the creation, manipulation, and study of complex networks _[Noise]: Random fluctuations in data that obscure the underlying signal _[Non-linear Fitting]: Curve fitting where the model depends non-linearly on its parameters _[Normalization]: Scaling data to a common range or standard, often between 0 and 1 or based on a specific feature _[NumPy]: Fundamental package for scientific computing with Python _[Objective Function]: The function that an optimization algorithm seeks to minimize or maximize (e.g., sum of squared residuals in Least-Squares) _[Optimizer]: The algorithm (e.g., Levenberg-Marquardt, Nelder-Mead) used by the Minimizer to find the best-fit Parameters _[Overfitting]: Fitting a model too closely to the noise or random fluctuations in the data, rather than the underlying trend _[Pandas DataFrames]: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structures in the Pandas Python library _[Parameter Space]: The multi-dimensional space defined by the possible values of the model parameters _[Parameters]: The adjustable variables within a Model (e.g., amplitude, center, width) that are optimized during fitting _[Peak Shape]: The functional form used to describe a peak (e.g., Gaussian, Lorentzian, Voigt) _[Peak Width]: A measure of the extent of a peak along the independent variable axis, often characterized by FWHM or sigma _[Photon]: A quantum of electromagnetic radiation _[Pip]: The standard package installer for Python _[Pickle File]: Python's built-in serialization format _[Plotly]: A graphing library for creating interactive charts and dashboards _[Poetry]: Tool for Python dependency management and packaging _[PPTX]: PowerPoint file format _[Preprocessing]: Steps taken to prepare raw data for analysis (e.g., normalization, baseline correction, smoothing) _[Pseudo-Voigt]: A peak function approximating the Voigt profile, calculated as a linear combination or sum of Gaussian and Lorentzian shapes _[Pydantic]: Data validation and settings management library for Python _[Python]: High-level, interpreted, general-purpose programming language _[pytest]: A testing framework for Python code _[Quantification]: Determining the amount or concentration of a substance based on spectral features _[R-squared (R²)]: Coefficient of determination; a statistical measure of how well the regression predictions approximate the real data points (1 indicates perfect fit) _[Raman Spectroscopy]: Spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system _[Reduced Chi-squared (χ²_ν)]: Chi-squared divided by the Degrees of Freedom, providing a measure of Goodness-of-fit normalized by the number of free parameters _[Regression]: Statistical process for estimating the relationships between variables _[Repository]: A central location where code and its history are stored, typically using a VCS like Git _[Resolution (Spectral)]: The ability of a spectrometer to distinguish between closely spaced spectral features _[Residuals]: The differences between the observed data values and the values predicted by the fitted Model _[RIXS]: Resonant Inelastic X-ray Scattering _[Robust Fitting]: Fitting methods less sensitive to outliers in the data compared to standard Least-Squares _[Savitzky-Golay Filter]: A digital filter used for smoothing data and computing derivatives _[Scattering]: Process where waves or particles are deflected from a straight path due to interaction with other matter _[SciPy]: Open-source Python library used for scientific and technical computing _[SDK]: Software Development Kit _[Sensitivity Analysis]: Study of how the uncertainty in the output of a model can be attributed to different sources of uncertainty in its inputs or parameters _[Signal Processing]: Manipulation and analysis of signals, such as spectroscopic data _[Sigma (σ)]: The standard deviation parameter, often related to the width (FWHM) of peak functions like the Gaussian _[Simulation]: Imitation of the operation of a real-world process or system, e.g., simulating spectra based on a model _[Smoothing]: Applying algorithms to reduce noise in data, often using moving averages or Savitzky-Golay filters _[SpectraFit]: Software for analyzing and fitting spectroscopic data _[Spectroscopy]: Study of the interaction between matter and electromagnetic radiation as a function of wavelength or frequency _[Spectrum]: Data representing intensity as a function of energy, wavelength, frequency, or wavenumber _[Standard Deviation]: A measure of the amount of variation or dispersion of a set of values _[Standard Error]: An estimate of the standard deviation of a fitted Parameter, indicating its uncertainty _[TOML]: Tom's Obvious, Minimal Language _[Tolerance]: A threshold used in optimization algorithms to determine when convergence has been reached _[Trunk-Based Development]: A source-control branching model where developers collaborate on code in a single branch called 'trunk' (or main) _[Uncertainty]: The range of possible values within which the true value of a measurement or parameter lies _[Units]: Standard quantities used for measurement (e.g., eV, nm, cm⁻¹) _[UV-Vis]: Ultraviolet-Visible Spectroscopy _[Validation]: Process of checking if the model and its results are acceptable for the intended purpose _[Vary]: A boolean flag for a Parameter indicating whether its value should be adjusted (True) or kept fixed (False) during optimization _[VCS]: Version Control System _[Version Control]: System for tracking changes to files over time, like Git _[Visualization]: Creating graphical representations of data or models _[Voigt]: A peak function resulting from the Convolution of a Gaussian and a Lorentzian profile _[VS Code]: Visual Studio Code, a source-code editor _[Wavelength]: Spatial period of a periodic wave, inversely related to frequency and energy _[Wavenumber]: Spatial frequency, often used as the independent variable (x-axis) in IR and Raman spectroscopy (e.g., cm⁻¹) _[Weighting]: Assigning different levels of importance to data points during fitting, often based on their uncertainty _[Width]: General term for the extent of a peak, often quantified by FWHM or sigma _[XAS]: X-ray Absorption Spectroscopy _[XPS]: X-ray Photoelectron Spectroscopy _[X-ray]: Form of electromagnetic radiation with high energy _[YAML]: YAML Ain't Markup Language _[Zero Filling]: Adding zeros to the end of a time-domain signal before Fourier Transform to increase spectral resolution (interpolation) *[lmfit]: A Python library for Non-Linear Least-Squares Minimization and Curve Fitting